A tool that allows you to create vulnerable instrumented local or cloud environments to simulate attacks against and collect the data into Splunk
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Updated
May 9, 2026 - Python
A tool that allows you to create vulnerable instrumented local or cloud environments to simulate attacks against and collect the data into Splunk
User Enumeration of Microsoft Teams users via API
A versatile command and control center (CCC) for DDoS Botnet Simulation & Load Generation.
Self-Learning AI for Manual Web Penetration Testing
AAPP‑MART is an AI-Autonomous Attack Path Prediction & Multi‑Agent Red Team Simulation Engine designed for attack simulation, automated threat modeling, adversary emulation, and enterprise‑grade cybersecurity validation aligned with MITRE ATT&CK.
versatile red team simulation tool for testing browser-based attacks. It supports payloads like keylogging, screenshot capturing, webcam access, clipboard hijacking, geolocation tracking, and more
🚀 Generate high-volume HTTP requests with Kaneki-DDoS, a user-friendly tool for network load testing featuring multiple modes and real-time logging.
Modbus/TCP attack simulation framework — reconnaissance, replay, FDI, command injection, DoS, and MitM against a live PLC server. No hardware required.
💀 Foundations for various nefarious programs in Python, for use in blue team exercises.
An Ethereum attack reproducer for security study.
AI Agent Security — Attack payloads, defense references, and research. 52 tests, ~10K lines. A learning-oriented shooting range, not a product.
Multi-stage ICS cyberattack simulation against 69kV/13.8kV distribution substation: IT-to-OT pivot culminating in unauthorized Modbus PLC manipulation. Includes PCAP forensic analysis, NIST incident response playbook, attack scripts, and real-world impact assessment for critical infrastructure security education.
Multi-agent attack simulation framework for AI systems. 8 attack categories, simulation engine, CLI, SARIF/JSON/HTML reporting, mcp-taxonomy integration.
End-to-end secured IoT pipeline over MQTT — mutual TLS, HMAC-SHA256 message authentication, replay attack simulation, and a real-time WebSocket threat dashboard.
Secure V2I - authenticated Diffie‑Hellman, RSA identity, AES+HMAC, replay protection
Applied threat analysis and security automation projects focused on proactive risk identification, threat modeling, and adversary-driven analysis using structured frameworks and scripting.
NoctisAPI is a realistic API honeypot designed to attract and observe real attackers through believable endpoints and silent telemetry.
A local-first prompt injection attack simulator for testing LLM security. Simulates 16+ attack types (jailbreak, role-play, indirect, multi-turn) with multi-layer defenses (regex, LLM guard, prompt engineering), ML classifier, attack success rate evaluation, and a Streamlit dashboard. Powered by Ollama — fully offline.
LLM-Enhanced Honeypot System - Complete research implementation with SSH emulation, 8 decoy artifacts, behavioral analysis, and attack simulation playbooks.
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